multiprocessing - why is time rising for more than one request to asyncio server in python?

Question

I wrote a pythonic server with socket. that should receives requests at the same time(parallel) and respond them parallel. When i send more than one request to it, the time of answering increase more than i expected.

server:

import datetime
import asyncio, timeit
import json, traceback
from asyncio import get_event_loop

requestslist = []
loop = asyncio.get_event_loop()

async def handleData(reader, writer):
    message = ''
    clientip = ''
    data = bytearray()
    print("Async HandleData", datetime.datetime.utcnow())


    try:
        start = timeit.default_timer()
        data = await reader.readuntil(separator=b'

')
        msg = data.decode(encoding='utf-8')
        len_csharp_message = int(msg[msg.find('content-length:') + 15:msg.find(';dmnid'):])
        data = await reader.read(len_csharp_message)
        message = data.decode(encoding='utf-8')

        clientip = reader._transport._extra['peername'][0]
        clientport = reader._transport._extra['peername'][1]
        print('
Data Received from:', clientip, ':', clientport)
        if (clientip, message) in requestslist:
            reader._transport._sock.close()

        else:
            requestslist.append((clientip, message))

            # adapter_result = parallel_members(message_dict, service, dmnid)
            adapter_result = '''[{"name": {"data": "data", "type": "str"}}]'''
            body = json.dumps(adapter_result, ensure_ascii=False)
            print(body)

            contentlen = len(bytes(str(body), 'utf-8'))
            header = bytes('Content-Length:{}'.format(contentlen), 'utf-8')
            result = header + bytes('

{', 'utf-8') + body + bytes('}', 'utf-8')
            stop = timeit.default_timer()
            print('total_time:', stop - start)
            writer.write(result)
            writer.close()
        writer.close()
        # del writer
    except Exception as ex:
        writer.close()
        print(traceback.format_exc())
    finally:
        try:
            requestslist.remove((clientip, message))
        except:
            pass


def main(*args):
    print("ready")
    loop = get_event_loop()
    coro = asyncio.start_server(handleData, 'localhost', 4040, loop=loop, limit=204800000)
    srv = loop.run_until_complete(coro)
    loop.run_forever()


if __name__ == '__main__':
    main()

When i send single request, it tooke 0.016 sec. but for more request, this time increase.

cpu info : intel xeon x5650

client:

import multiprocessing, subprocess
import time
from joblib import Parallel, delayed


def worker(file):
    subprocess.Popen(file, shell=False)


def call_parallel (index):
    print('begin ' , index)
    p = multiprocessing.Process(target=worker(index))
    p.start()
    print('end ' , index)

path = r'python "/test-Client.py"'     # ## client address
files = [path, path, path, path, path, path, path, path, path, path, path, path]
Parallel(n_jobs=-1, backend="threading")(delayed(call_parallel)(i) for index,i  in  enumerate(files))

for this client that send 12 requests synchronous, total time for per request is 0.15 sec.

I expect for any number requests, the time be fixed.

Answer 1

What is request

Single request (roughly saying) consists of the following steps:

1. write data to network
2. waste time waiting for answer
3. read answer from network

№1/№3 processed by your CPU very fast. Step №2 - is a bytes journey from your PC to some server (in another city, for example) and back by wires: it usually takes much more time.

How asynchronous requests work

Asynchronous requests are not really "parallel" in terms of processing: it's still your single CPU core that can process one thing at a time. But running multiple async requests allows you to use step №2 of some request to do steps №1/№3 of other request instead of just wasting huge amount of time. That's a reason why multiple async requests usually would finish earlier then same amount of sync ones.

Running async code without network delay

But when you run things locally, step №2 doesn't take much time: your PC and server are the same thing and bytes don't go to network journey. There is just no time that can be used in step №2 to start new request. Only your single CPU core works processing one thing at a time.

You should test requests against server that answers with some delay to see results you expect.